The method implemented in RJaCGH allows us to incorporate distance between probes/clones (the non-homogenous in non-homogeneous HMM). We return posterior probabilities that a gene/region is altered, using Markov Chain Monte Carlo. The method does not require you to pre-specify the number of states, as we use Reversible Jump for transdimensional moves. Finally, we use Bayesian Model Averaging for incorporating model uncertainty.